Theories of Person Perception Predict Patterns of Neural Activity During Mentalizing
Theories of Person Perception Predict Patterns of Neural Activity During Mentalizing
复制标题
DOI：
10.1093/cercor/bhx216
复制
发表时间：
2018-10-01
期刊：
影响因子：
3.7
通讯作者：
Mitchell, Jason P.
中科院分区：
文献类型：
--
作者：
Thornton, Mark A.;Mitchell, Jason P.
社交生活需要对他人做出推断。感知者自发地利用什么信息来做出这样的推论？在这里，我们测试4个主要的理论的人的知觉，和1个综合理论，结合他们的特点，以确定这些理论的尺寸是否可以作为基础，用于描述模式的神经活动在心智化。在接受功能性磁共振成像时，参与者对知名公众人物做出社会判断。然后使用特征编码模型预测大脑活动的模式，这些模型代表了目标人群在理论维度上的位置，如温暖和能力。所有5个理论的人的知觉被证明是高度准确的重建活动模式，这表明每一个可以描述的信息基础的心智化。交叉验证表明，这些理论在目标和参与者之间都得到了有力的推广。综合理论始终达到最佳性能-约三分之二的噪音上限的准确性-这表明，在组合，这里考虑的理论可以解释其他人的神经表征。此外，在当前数据上训练的编码模型可以重建与独立数据中的精神状态表征相关的活动模式，这表明使用共同的神经代码来表示其他人的特质和状态。
Social life requires making inferences about other people. What information do perceivers spontaneously draw upon to make such inferences? Here, we test 4 major theories of person perception, and 1 synthetic theory that combines their features, to determine whether the dimensions of such theories can serve as bases for describing patterns of neural activity during mentalizing. While undergoing functional magnetic resonance imaging, participants made social judgments about well-known public figures. Patterns of brain activity were then predicted using feature encoding models that represented target people's positions on theoretical dimensions such as warmth and competence. All 5 theories of person perception proved highly accurate at reconstructing activity patterns, indicating that each could describe the informational basis of mentalizing. Cross-validation indicated that the theories robustly generalized across both targets and participants. The synthetic theory consistently attained the best performance-approximately two-thirds of noise ceiling accuracy- indicating that, in combination, the theories considered here can account for much of the neural representation of other people. Moreover, encoding models trained on the present data could reconstruct patterns of activity associated with mental state representations in independent data, suggesting the use of a common neural code to represent others' traits and states.